Extreme Groups Split Means
Catherine Walsh
4/1/2020
Another question we had was to see whether splitting into smaller, more extreme groups would show morer extreme differences across them.
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, unionlibrary(ggplot2)
library(reshape2)
library(psych)##
## Attaching package: 'psych'## The following objects are masked from 'package:ggplot2':
##
## %+%, alphalibrary(patchwork)
library(rockchalk)##
## Attaching package: 'rockchalk'## The following object is masked from 'package:dplyr':
##
## summarizeload('data/load_effects_DFR.RData')
load('data/behav.RData')
load('data/structural_measures.RData')
load('data/connectivity_data.RData')
source("split_into_groups.R")
source("prep_split_for_bar_plots.R")
source("plot_bars.R")constructs_fMRI <- construct_vars_omnibus[construct_vars_omnibus$PTID %in% p200_indiv_ROI_DFR_delay$PTID,]
data_for_plot <- merge(p200_indiv_ROI_DFR_delay,constructs_fMRI)
data_for_plot <- merge(data_for_plot,things_to_hist[,c(1,8)],by="PTID",all=TRUE)
data_for_plot$level <- factor(data_for_plot$level, levels = c("high","med","low"))
p1 <- ggplot(data_for_plot, aes(x=omnibus_span_no_DFR_MRI, y = DFR_ROIs))+
geom_point(aes(color=level))+
stat_smooth(method="loess")
p2 <- ggplot(data_for_plot, aes(x=omnibus_span_no_DFR_MRI, y = DFR_L_dlPFC))+
geom_point(aes(color=level))+
stat_smooth(method="loess")
p3 <- ggplot(data_for_plot, aes(x=omnibus_span_no_DFR_MRI, y = DFR_L_IPS))+
geom_point(aes(color=level))+
stat_smooth(method="loess")
p4 <- ggplot(data_for_plot, aes(x=omnibus_span_no_DFR_MRI, y = DFR_L_preSMA))+
geom_point(aes(color=level))+
stat_smooth(method="loess")
(p1+p2)/(p3+p4)
split_constructs <- split_into_groups(constructs_fMRI[1:7],extreme_groups)
split_clinical <- split_into_groups(p200_clinical_zscores, extreme_groups)
split_DFR_delay <- split_into_groups(p200_indiv_ROI_DFR_delay, extreme_groups)
split_DFR_cue <- split_into_groups(p200_indiv_ROI_DFR_cue, extreme_groups)
split_DFR_probe <- split_into_groups(p200_indiv_ROI_DFR_probe, extreme_groups)
split_DFR_FFA <- split_into_groups(p200_FFA,extreme_groups)
split_DFR_HPC_Ant <- split_into_groups(p200_HPC_Ant, extreme_groups)
split_DFR_HPC_Med <- split_into_groups(p200_HPC_Med, extreme_groups)
split_DFR_HPC_Post <- split_into_groups(p200_HPC_Post, extreme_groups)
split_fullMask_delay <- split_into_groups(p200_DFR_full_mask, extreme_groups)
split_cue_ROIs <- split_into_groups(p200_indiv_ROI_delayDFR_cuePeriod, extreme_groups)
split_demographics <- split_into_groups(p200_demographics,extreme_groups)
split_cortical_thickness_DFR <- split_into_groups(p200_DFR_fullMask_cortical_thickness,extreme_groups)
split_RS <- split_into_groups(p200_all_RS,extreme_groups)
split_beta_conn_cue <- split_into_groups(p200_beta_conn_cue,extreme_groups)
split_beta_conn_delay <- split_into_groups(p200_beta_conn_delay,extreme_groups)
split_BCT <- split_into_groups(p200_BCT_forCorr,extreme_groups)
split_indiv_partic_coeff <- split_into_groups(p200_indiv_network_ParticCoeff,extreme_groups)save(list=c("split_constructs","split_clinical","split_DFR_delay", "split_DFR_cue", "split_DFR_probe", "split_DFR_FFA", "split_DFR_HPC_Ant", "split_DFR_HPC_Med", "split_DFR_HPC_Post", "split_fullMask_delay", "split_cue_ROIs", "split_demographics","split_cortical_thickness_DFR","split_RS","split_beta_conn_cue","split_beta_conn_delay","split_BCT", "split_indiv_partic_coeff"), file="data/split_extreme_groups_fMRI.RData")split_means_demo <- data.frame(matrix(nrow=length(split_demographics)-1,ncol=8))
colnames(split_means_demo) <- c("Trio","Prisma","CS","NCS","female","male","age","age_se")
rownames(split_means_demo) <- names(split_demographics)[1:length(names(split_demographics))-1]
for (level in seq.int(1,length(split_demographics)-1)){
split_means_demo$Trio[level] <- length(split_demographics[[level]]$SCANNER[split_demographics[[level]]$SCANNER==1])
split_means_demo$Prisma[level] <- length(split_demographics[[level]]$SCANNER[split_demographics[[level]]$SCANNER==2])
split_means_demo$CS[level] <- length(split_demographics[[level]]$GROUP[split_demographics[[level]]$GROUP==1])
split_means_demo$NCS[level] <- length(split_demographics[[level]]$GROUP[split_demographics[[level]]$GROUP==2])
split_means_demo$female[level] <- length(split_demographics[[level]]$GENDER[split_demographics[[level]]$GENDER==2])
split_means_demo$male[level] <- length(split_demographics[[level]]$GENDER[split_demographics[[level]]$GENDER==1])
split_means_demo$age[level] <- mean(split_demographics[[level]]$AGE,na.rm=TRUE)
split_means_demo$age_se[level] <- sd(split_demographics[[level]]$AGE,na.rm=TRUE)/sqrt(length(split_demographics[[level]]$AGE[!is.na(split_demographics[[level]]$AGE)]))
}
split_means_demo$level <- as.factor(c("high", "med","low"))
means_melt_demo <- melt(split_means_demo,id.vars="level")Demographics
age_plot <- ggplot(data=split_means_demo,aes(x=level,y=age))+
geom_bar(stat="identity",width = .5, color = "#667Ea4", fill = "#667Ea4")+
geom_errorbar(aes(ymin=age-age_se,ymax=age+age_se),width=.2)+
ggtitle("Age") +
ylab("Mean +/- SE") +
scale_x_discrete(limits = c("low","med","high")) +
theme(aspect.ratio = 1)
scanner_data <- demo_plot_data.m[demo_plot_data.m$variable=="scanner_count",c(1,2,5,6)]
scanner_data$value <- scanner_data$value/56*100
scanner_plot <- ggplot(scanner_data,aes(x=level,y=value,fill=scanner))+
geom_bar(stat="identity") +
ylab("Percent (%)") +
theme(aspect.ratio=1) +
scale_x_discrete(limits = c("low","med","high")) +
ggtitle("Scanner")
gender_data <- demo_plot_data.m[demo_plot_data.m$variable=="gender_count",c(1,3,5,6)]
gender_data$value <- gender_data$value/56*100
gender_plot <- ggplot(gender_data,aes(x=level,y=value, fill=gender))+
geom_bar(stat="identity") +
ylab("Percent (%)") +
theme(aspect.ratio=1) +
scale_x_discrete(limits = c("low","med","high")) +
ggtitle("Gender")
care_data <- demo_plot_data.m[demo_plot_data.m$variable=="care_count",c(1,4:6)]
care_data$value <- care_data$value/56*100
care_plot <- ggplot(care_data,aes(x=level,y=value, fill=care))+
geom_bar(stat="identity") +
ylab("Percent (%)") +
theme(aspect.ratio=1) +
scale_x_discrete(limits = c("low","med","high")) +
ggtitle("CS vs NCS")
(age_plot + gender_plot)/(care_plot + scanner_plot)+
plot_annotation(title="Demographics split by DFR performance")
melt_constructs <- prep_split_for_bar_plots(extreme_groups)
melt_clinical <- prep_split_for_bar_plots(split_clinical)
melt_DFR_delay <- prep_split_for_bar_plots(split_DFR_delay)
melt_DFR_cue <- prep_split_for_bar_plots(split_DFR_cue)
melt_DFR_probe <- prep_split_for_bar_plots(split_DFR_probe)
melt_DFR_FFA <- prep_split_for_bar_plots(split_DFR_FFA)
melt_DFR_HPC_Ant <- prep_split_for_bar_plots(split_DFR_HPC_Ant)
melt_DFR_HPC_Med <- prep_split_for_bar_plots(split_DFR_HPC_Med)
melt_DFR_HPC_Post <- prep_split_for_bar_plots(split_DFR_HPC_Post)
melt_fullMask_delay <- prep_split_for_bar_plots(split_fullMask_delay)
melt_cue_ROIs <- prep_split_for_bar_plots(split_cue_ROIs)
melt_cortical_thickness_DFR <- prep_split_for_bar_plots(split_cortical_thickness_DFR)
melt_RS <- prep_split_for_bar_plots(split_RS)
melt_beta_conn_cue <- prep_split_for_bar_plots(split_beta_conn_cue)
melt_beta_conn_delay <- prep_split_for_bar_plots(split_beta_conn_delay)
melt_BCT <- prep_split_for_bar_plots(split_BCT)
melt_indiv_partic_coeff <- prep_split_for_bar_plots(split_indiv_partic_coeff)constructs_plots <- plot_bars(melt_constructs)
clinical_plots <- plot_bars(melt_clinical)
DFR_delay_plots <- plot_bars(melt_DFR_delay)
DFR_cue_plots <- plot_bars(melt_DFR_cue)
DFR_probe_plots <- plot_bars(melt_DFR_probe)
DFR_FFA_plots <- plot_bars(melt_DFR_FFA)
DFR_HPC_Ant_plots <- plot_bars(melt_DFR_HPC_Ant)
DFR_HPC_Med_plots <- plot_bars(melt_DFR_HPC_Med)
DFR_HPC_Post_plots <- plot_bars(melt_DFR_HPC_Post)
fullMask_delay_plots <- plot_bars(melt_fullMask_delay)
cue_ROIs_plots <- plot_bars(melt_cue_ROIs)
cortical_thickness_plots <- plot_bars(melt_cortical_thickness_DFR)
RS_plots <- plot_bars(melt_RS)
beta_conn_cue_plots <- plot_bars(melt_beta_conn_cue)
beta_conn_delay_plots <- plot_bars(melt_beta_conn_delay)
BCT_plots <- plot_bars(melt_BCT)
indiv_partic_coeff_plots <- plot_bars(melt_indiv_partic_coeff)Constructs
A nice sanity check here as well - if a subject has higher capacity and higher intelligence, they tend to have higher performance. The main statistically significant differences here are in omnibus span, where high > low.
constructs_plots[["omnibus_span_no_DFR_MRI"]]$labels$title = "Omnibus Span"
(constructs_plots[["omnibus_span_no_DFR_MRI"]]+constructs_plots[["intelligence"]]+constructs_plots[["LTM"]]) +
plot_annotation(title="Constructs split on DFR performance")
print("Omnibus Span")## [1] "Omnibus Span"span.aov <- aov(omnibus_span_no_DFR_MRI ~ level, data=split_constructs[["all"]])
summary(span.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 40.24 20.121 413.8 <2e-16 ***
## Residuals 117 5.69 0.049
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(span.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = omnibus_span_no_DFR_MRI ~ level, data = split_constructs[["all"]])
##
## $level
## diff lwr upr p adj
## med-high -0.6960934 -0.8131510 -0.5790359 0
## low-high -1.4184201 -1.5354776 -1.3013625 0
## low-med -0.7223266 -0.8393842 -0.6052691 0print("LTM")## [1] "LTM"LTM.aov <- aov(LTM ~ level, data=split_constructs[["all"]])
summary(LTM.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 7.55 3.776 7.377 0.000971 ***
## Residuals 114 58.34 0.512
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 3 observations deleted due to missingnessTukeyHSD(LTM.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = LTM ~ level, data = split_constructs[["all"]])
##
## $level
## diff lwr upr p adj
## med-high -0.4460251 -0.8283300 -0.06372028 0.0178226
## low-high -0.5959517 -0.9807951 -0.21110828 0.0010451
## low-med -0.1499266 -0.5371662 0.23731309 0.6291854print("Intelligence")## [1] "Intelligence"intelligence.aov <- aov(intelligence ~ level, data=split_constructs[["all"]])
summary(intelligence.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 23.01 11.506 30.08 2.88e-11 ***
## Residuals 117 44.75 0.382
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(intelligence.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = intelligence ~ level, data = split_constructs[["all"]])
##
## $level
## diff lwr upr p adj
## med-high -0.3647352 -0.6930224 -0.03644802 0.0255139
## low-high -1.0559533 -1.3842405 -0.72766613 0.0000000
## low-med -0.6912181 -1.0195053 -0.36293091 0.0000061Clinical
clinical_plots[["WHO_ST_S32"]]$labels$title <- "WHODAS"
clinical_plots[["BPRS"]]$labels$title <- "BPRS"
(clinical_plots[["WHO_ST_S32"]] + clinical_plots[["BPRS_TOT"]])+
plot_annotation(title="Clinical measures split by DFR performance")
print("WHODAS")## [1] "WHODAS"WHODAS.aov <- aov(WHO_ST_S32 ~ level, data=split_clinical[["all"]])
summary(WHODAS.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 3.76 1.880 1.873 0.158
## Residuals 117 117.46 1.004print("BPRS")## [1] "BPRS"BPRS.aov <- aov(BPRS_TOT ~ level, data=split_clinical[["all"]])
summary(BPRS.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 8.75 4.373 4.261 0.0164 *
## Residuals 117 120.08 1.026
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Cue Period
Full mask
There is a linear relationship between performance and load effect in the cue mask during the delay period.
fullMask_delay_plots[["cue_low"]]+fullMask_delay_plots[["cue_high"]]+fullMask_delay_plots[["cue_loadEffect"]]+
plot_annotation(title="BOLD signal from full delay period mask during cue period")
print("Load Effect")## [1] "Load Effect"cue_LE.aov <- aov(cue_loadEffect ~ level, data=split_fullMask_delay[["all"]])
summary(cue_LE.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.409 0.2044 1.429 0.244
## Residuals 117 16.737 0.1431TukeyHSD(cue_LE.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = cue_loadEffect ~ level, data = split_fullMask_delay[["all"]])
##
## $level
## diff lwr upr p adj
## med-high 0.05027093 -0.1504947 0.2510366 0.8233839
## low-high -0.09078882 -0.2915545 0.1099768 0.5323653
## low-med -0.14105976 -0.3418254 0.0597059 0.2218776Individual ROIs
DFR_cue_plots[["L_FEF_low"]] + DFR_cue_plots[["L_FEF_high"]] + DFR_cue_plots[["L_FEF_loadEffect"]]
DFR_cue_plots[["L_insula_low"]] + DFR_cue_plots[["L_insula_high"]] + DFR_cue_plots[["L_insula_loadEffect"]]
DFR_cue_plots[["L_IPS_low"]] + DFR_cue_plots[["L_IPS_high"]] + DFR_cue_plots[["L_IPS_loadEffect"]]
DFR_cue_plots[["L_occipital_low"]] + DFR_cue_plots[["L_occipital_high"]] + DFR_cue_plots[["L_occipital_loadEffect"]]
DFR_cue_plots[["R_FEF_low"]] + DFR_cue_plots[["R_FEF_high"]] + DFR_cue_plots[["R_FEF_loadEffect"]]
DFR_cue_plots[["R_insula_low"]] + DFR_cue_plots[["R_insula_high"]] + DFR_cue_plots[["R_insula_loadEffect"]]
DFR_cue_plots[["R_IPS_low"]] + DFR_cue_plots[["R_IPS_high"]] + DFR_cue_plots[["R_IPS_loadEffect"]]
DFR_cue_plots[["R_MFG_low"]] + DFR_cue_plots[["R_MFG_high"]] + DFR_cue_plots[["R_MFG_loadEffect"]]
DFR_cue_plots[["R_preSMA_low"]] + DFR_cue_plots[["R_preSMA_high"]] + DFR_cue_plots[["R_preSMA_loadEffect"]]
DFR_cue_plots[["R_occipital_low"]] + DFR_cue_plots[["R_occipital_high"]] + DFR_cue_plots[["R_occipital_loadEffect"]]
print("L FEF")## [1] "L FEF"cue_L_FEF.aov <- aov(L_FEF_loadEffect ~ level, data=split_DFR_cue[["all"]])
summary(cue_L_FEF.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.067 0.03369 0.156 0.856
## Residuals 117 25.300 0.21624print("L insula")## [1] "L insula"cue_L_insula.aov <- aov(L_insula_loadEffect ~ level, data=split_DFR_cue[["all"]])
summary(cue_L_insula.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.12 0.06129 0.224 0.8
## Residuals 117 32.06 0.27403print("L IPS")## [1] "L IPS"cue_L_IPS.aov <- aov(L_IPS_loadEffect ~ level, data=split_DFR_cue[["all"]])
summary(cue_L_IPS.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.1 0.0506 0.114 0.892
## Residuals 117 51.9 0.4436print("L occipital")## [1] "L occipital"cue_L_occipital.aov <- aov(L_occipital_loadEffect ~ level, data=split_DFR_cue[["all"]])
summary(cue_L_occipital.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.34 0.6688 1.389 0.253
## Residuals 117 56.33 0.4815print("R FEF")## [1] "R FEF"cue_R_FEF.aov <- aov(R_FEF_loadEffect ~ level, data=split_DFR_cue[["all"]])
summary(cue_R_FEF.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.32 0.1621 0.264 0.769
## Residuals 117 71.97 0.6151print("R insula")## [1] "R insula"cue_R_insula.aov <- aov(R_insula_loadEffect ~ level, data=split_DFR_cue[["all"]])
summary(cue_R_insula.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.031 0.01563 0.066 0.936
## Residuals 117 27.518 0.23520print("R IPS")## [1] "R IPS"cue_R_IPS.aov <- aov(R_IPS_loadEffect ~ level, data=split_DFR_cue[["all"]])
summary(cue_R_IPS.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.11 0.0540 0.122 0.885
## Residuals 117 51.75 0.4423print("R MFG")## [1] "R MFG"cue_R_MFG.aov <- aov(R_MFG_loadEffect ~ level, data=split_DFR_cue[["all"]])
summary(cue_R_MFG.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.278 0.1389 0.72 0.489
## Residuals 117 22.578 0.1930print("R occipital")## [1] "R occipital"cue_R_occipital.aov <- aov(R_occipital_loadEffect ~ level, data=split_DFR_cue[["all"]])
summary(cue_R_occipital.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.74 0.3676 0.798 0.453
## Residuals 117 53.92 0.4608print("R preSMA")## [1] "R preSMA"cue_R_preSMA.aov <- aov(R_preSMA_loadEffect ~ level, data=split_DFR_cue[["all"]])
summary(cue_R_preSMA.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.21 0.1053 0.215 0.807
## Residuals 117 57.31 0.4898Delay Period
Full Mask
There are significant differnces in the high load and load effect - the high load trials have differences: high > low and high > medium, while the load effect only has high > low.
fullMask_delay_plots[["delay_low"]]+fullMask_delay_plots[["delay_high"]]+fullMask_delay_plots[["delay_loadEffect"]]+
plot_annotation(title="BOLD signal from full delay period mask during delay period")
print("Low Load")## [1] "Low Load"delay_L1.aov <- aov(delay_low ~ level, data=split_fullMask_delay[["all"]])
summary(delay_L1.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0525 0.02627 1.041 0.356
## Residuals 117 2.9537 0.02525print("High Load")## [1] "High Load"delay_L3.aov <- aov(delay_high ~ level, data=split_fullMask_delay[["all"]])
summary(delay_L3.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.127 0.06364 1.594 0.208
## Residuals 117 4.672 0.03993print("Load Effect")## [1] "Load Effect"delay_LE.aov <- aov(delay_loadEffect ~ level, data=split_fullMask_delay[["all"]])
summary(delay_LE.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.260 0.1301 2.53 0.084 .
## Residuals 117 6.014 0.0514
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Individual ROIs
No L dMFG, all show high > low, except for . L aMFG, L dlPFC, R dlPFC also showed high > med, and R medial parietal only showed high > med.
(DFR_delay_plots[["DFR_L_aMFG"]] + DFR_delay_plots[["DFR_L_dlPFC"]] + DFR_delay_plots[["DFR_L_dMFG"]]) + plot_annotation(title="individual DFR delay period ROIs")
(DFR_delay_plots[["DFR_L_IPS"]] + DFR_delay_plots[["DFR_L_preSMA"]] + DFR_delay_plots[["DFR_R_dlPFC"]]) 
(DFR_delay_plots[["DFR_R_dMFG"]] + DFR_delay_plots[["DFR_R_IPS"]] + DFR_delay_plots[["DFR_R_medParietal"]]) 
print("L aMFG")## [1] "L aMFG"L_aMFG.aov <- aov(DFR_L_aMFG ~ level, data=split_DFR_delay[["all"]])
summary(L_aMFG.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.704 0.3518 4.144 0.0182 *
## Residuals 117 9.932 0.0849
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(L_aMFG.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = DFR_L_aMFG ~ level, data = split_DFR_delay[["all"]])
##
## $level
## diff lwr upr p adj
## med-high 0.05621849 -0.09844019 0.21087717 0.6646739
## low-high -0.12685026 -0.28150895 0.02780842 0.1302187
## low-med -0.18306876 -0.33772744 -0.02841007 0.0159052print("L dlPFC")## [1] "L dlPFC"L_dlPFC.aov <- aov(DFR_L_dlPFC ~ level, data=split_DFR_delay[["all"]])
summary(L_dlPFC.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.278 0.13909 1.927 0.15
## Residuals 117 8.445 0.07218print("L dMFG")## [1] "L dMFG"L_dMFG.aov <- aov(DFR_L_dMFG ~ level, data=split_DFR_delay[["all"]])
summary(L_dMFG.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.308 0.15397 2.839 0.0625 .
## Residuals 117 6.346 0.05424
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1print("L IPS")## [1] "L IPS"L_IPS.aov <- aov(DFR_L_IPS ~ level, data=split_DFR_delay[["all"]])
summary(L_IPS.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.363 0.18152 3.362 0.038 *
## Residuals 117 6.317 0.05399
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(L_IPS.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = DFR_L_IPS ~ level, data = split_DFR_delay[["all"]])
##
## $level
## diff lwr upr p adj
## med-high 0.09314226 -0.03019883 0.216483355 0.1764976
## low-high -0.03773438 -0.16107547 0.085606715 0.7484446
## low-med -0.13087664 -0.25421773 -0.007535549 0.0347881print("L preSMA")## [1] "L preSMA"L_preSMA.aov <- aov(DFR_L_preSMA ~ level, data=split_DFR_delay[["all"]])
summary(L_preSMA.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.393 0.19670 3.298 0.0404 *
## Residuals 117 6.978 0.05964
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(L_preSMA.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = DFR_L_preSMA ~ level, data = split_DFR_delay[["all"]])
##
## $level
## diff lwr upr p adj
## med-high 0.03055281 -0.09907881 0.160184442 0.8417916
## low-high -0.10326650 -0.23289813 0.026365129 0.1457828
## low-med -0.13381931 -0.26345094 -0.004187685 0.0413687print("R dlPFC")## [1] "R dlPFC"R_dlPFC.aov <- aov(DFR_R_dlPFC ~ level, data=split_DFR_delay[["all"]])
summary(R_dlPFC.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.339 0.16943 1.993 0.141
## Residuals 117 9.944 0.08499print("R dMFG")## [1] "R dMFG"R_dMFG.aov <- aov(DFR_R_dMFG ~ level, data=split_DFR_delay[["all"]])
summary(R_dMFG.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.201 0.10060 1.359 0.261
## Residuals 117 8.661 0.07403print("R IPS")## [1] "R IPS"R_IPS.aov <- aov(DFR_R_IPS ~ level, data=split_DFR_delay[["all"]])
summary(R_IPS.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.283 0.1414 1.846 0.162
## Residuals 117 8.962 0.0766print("R medial Parietal")## [1] "R medial Parietal"R_medParietal.aov <- aov(DFR_R_medParietal ~ level, data=split_DFR_delay[["all"]])
summary(R_medParietal.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.254 0.1271 0.611 0.545
## Residuals 117 24.350 0.2081Probe Period
Full Mask
No differences in the probe period.
fullMask_delay_plots[["probe_low"]]+fullMask_delay_plots[["probe_high"]]+fullMask_delay_plots[["probe_loadEffect"]]+
plot_annotation(title="BOLD signal from full delay period mask during probe period")
print("Low Load")## [1] "Low Load"probe_L1.aov <- aov(probe_low ~ level, data=split_fullMask_delay[["all"]])
summary(probe_L1.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.18 0.0876 0.21 0.811
## Residuals 117 48.72 0.4164print("High Load")## [1] "High Load"probe_L3.aov <- aov(probe_high ~ level, data=split_fullMask_delay[["all"]])
summary(probe_L3.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 2.86 1.4324 1.948 0.147
## Residuals 117 86.02 0.7352print("Load Effect")## [1] "Load Effect"probe_LE.aov <- aov(probe_loadEffect ~ level, data=split_fullMask_delay[["all"]])
summary(probe_LE.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 2.22 1.1086 1.72 0.184
## Residuals 117 75.43 0.6447Individual ROIs
DFR_probe_plots[["dmPFC_loadEffect"]] + DFR_probe_plots[["L_aMFG_loadEffect"]] + DFR_probe_plots[["L_dlPFC_loadEffect"]] +
plot_annotation(title="individual DFR activity from probe period regions")
DFR_probe_plots[["L_insula_loadEffect"]] + DFR_probe_plots[["L_IPS_loadEffect"]] + DFR_probe_plots[["R_dlPFC_loadEffect"]] 
DFR_probe_plots[["R_insula_loadEffect"]] + DFR_probe_plots[["R_OFC_loadEffect"]] 
print("dmPFC")## [1] "dmPFC"probe_dmPFC.aov <- aov(dmPFC_loadEffect ~ level, data=split_DFR_probe[["all"]])
summary(probe_dmPFC.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.71 0.3534 1.154 0.319
## Residuals 117 35.84 0.3063print("L aMFG")## [1] "L aMFG"probe_L_aMFG.aov <- aov(L_aMFG_loadEffect ~ level, data=split_DFR_probe[["all"]])
summary(probe_L_aMFG.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.84 0.4216 0.558 0.574
## Residuals 117 88.40 0.7556print("L dlPFC")## [1] "L dlPFC"probe_L_dlPFC.aov <- aov(L_dlPFC_loadEffect ~ level, data=split_DFR_probe[["all"]])
summary(probe_L_dlPFC.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.75 0.3734 0.819 0.443
## Residuals 117 53.34 0.4559print("L insula")## [1] "L insula"probe_L_insula.aov <- aov(L_insula_loadEffect ~ level, data=split_DFR_probe[["all"]])
summary(probe_L_insula.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.067 0.03365 0.177 0.838
## Residuals 117 22.286 0.19048print("R dlPFC")## [1] "R dlPFC"probe_R_dlPFC.aov <- aov(R_dlPFC_loadEffect ~ level, data=split_DFR_probe[["all"]])
summary(probe_R_dlPFC.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.22 0.1101 0.269 0.765
## Residuals 117 47.94 0.4097print("R Insula")## [1] "R Insula"probe_R_insula.aov <- aov(R_insula_loadEffect ~ level, data=split_DFR_probe[["all"]])
summary(probe_R_insula.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.087 0.04338 0.246 0.782
## Residuals 117 20.620 0.17624print("R OFC")## [1] "R OFC"probe_R_OFC.aov <- aov(R_OFC_loadEffect ~ level, data=split_DFR_probe[["all"]])
summary(probe_R_OFC.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.235 0.1174 0.562 0.571
## Residuals 117 24.427 0.2088FFA
No differences.
DFR_FFA_plots[["L_CUE_LE"]] + DFR_FFA_plots[["L_DELAY_LE"]] + DFR_FFA_plots[["L_PROBE_LE"]]+
plot_annotation(title="FFA during DFR task")
DFR_FFA_plots[["R_CUE_LE"]] + DFR_FFA_plots[["R_DELAY_LE"]] + DFR_FFA_plots[["R_PROBE_LE"]]
print("L Cue")## [1] "L Cue"L_CUE_LE_FFA.aov <- aov(L_CUE_LE ~ level, data=split_DFR_FFA[["all"]])
summary(L_CUE_LE_FFA.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.93 0.4650 1.57 0.212
## Residuals 116 34.35 0.2961print("R Cue")## [1] "R Cue"R_CUE_LE_FFA.aov <- aov(R_CUE_LE ~ level, data=split_DFR_FFA[["all"]])
summary(R_CUE_LE_FFA.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.24 0.1222 0.427 0.654
## Residuals 116 33.21 0.2863print("L Delay")## [1] "L Delay"L_DELAY_LE_FFA.aov <- aov(L_DELAY_LE ~ level, data=split_DFR_FFA[["all"]])
summary(L_DELAY_LE_FFA.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.007 0.00374 0.086 0.918
## Residuals 116 5.051 0.04354print("R Delay")## [1] "R Delay"R_DELAY_LE_FFA.aov <- aov(R_DELAY_LE ~ level, data=split_DFR_FFA[["all"]])
summary(R_DELAY_LE_FFA.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.002 0.00091 0.023 0.977
## Residuals 116 4.533 0.03908print("L Probe")## [1] "L Probe"L_PROBE_LE_FFA.aov <- aov(L_PROBE_LE ~ level, data=split_DFR_FFA[["all"]])
summary(L_PROBE_LE_FFA.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 2.18 1.0919 1.666 0.194
## Residuals 116 76.04 0.6555print("R Probe")## [1] "R Probe"R_PROBE_LE_FFA.aov <- aov(R_PROBE_LE ~ level, data=split_DFR_FFA[["all"]])
summary(R_PROBE_LE_FFA.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.32 0.6596 1.256 0.289
## Residuals 116 60.90 0.5250HPC
Seeing differences in HPC activity in posterior segment for cue and probe L3.
Anterior
L3
DFR_HPC_Ant_plots[["L_CUE_L3"]] + DFR_HPC_Ant_plots[["L_DELAY_L3"]] + DFR_HPC_Ant_plots[["L_PROBE_L3"]]+
plot_annotation(title="HPC Ant during DFR task")
DFR_HPC_Ant_plots[["R_CUE_L3"]] + DFR_HPC_Ant_plots[["R_DELAY_L3"]] + DFR_HPC_Ant_plots[["R_PROBE_L3"]]
print("L Cue")## [1] "L Cue"L_CUE_L3_HPC_Ant.aov <- aov(L_CUE_L3 ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(L_CUE_L3_HPC_Ant.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.179 0.08956 0.387 0.68
## Residuals 116 26.867 0.23162print("R Cue")## [1] "R Cue"R_CUE_L3_HPC_Ant.aov <- aov(R_CUE_L3 ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(R_CUE_L3_HPC_Ant.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.056 0.02785 0.133 0.875
## Residuals 116 24.262 0.20915print("L Delay")## [1] "L Delay"L_DELAY_L3_HPC_Ant.aov <- aov(L_DELAY_L3 ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(L_DELAY_L3_HPC_Ant.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.020 0.009807 0.328 0.721
## Residuals 116 3.469 0.029902print("R Delay")## [1] "R Delay"R_DELAY_L3_HPC_Ant.aov <- aov(R_DELAY_L3 ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(R_DELAY_L3_HPC_Ant.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0844 0.04219 1.657 0.195
## Residuals 116 2.9526 0.02545print("L Probe")## [1] "L Probe"L_PROBE_L3_HPC_Ant.aov <- aov(L_PROBE_L3 ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(L_PROBE_L3_HPC_Ant.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.56 0.2800 0.545 0.582
## Residuals 116 59.64 0.5141print("R Probe")## [1] "R Probe"R_PROBE_L3_HPC_Ant.aov <- aov(R_PROBE_L3 ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(R_PROBE_L3_HPC_Ant.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.08 0.0410 0.071 0.931
## Residuals 116 66.77 0.5756LE
DFR_HPC_Ant_plots[["L_CUE_LE"]] + DFR_HPC_Ant_plots[["L_DELAY_LE"]] + DFR_HPC_Ant_plots[["L_PROBE_LE"]]+
plot_annotation(title="HPC Ant during DFR task")
DFR_HPC_Ant_plots[["R_CUE_LE"]] + DFR_HPC_Ant_plots[["R_DELAY_LE"]] + DFR_HPC_Ant_plots[["R_PROBE_LE"]]
print("L Cue")## [1] "L Cue"L_CUE_LE_HPC_Ant.aov <- aov(L_CUE_LE ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(L_CUE_LE_HPC_Ant.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.421 0.2103 1.249 0.291
## Residuals 116 19.534 0.1684print("R Cue")## [1] "R Cue"R_CUE_LE_HPC_Ant.aov <- aov(R_CUE_LE ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(R_CUE_LE_HPC_Ant.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.165 0.08263 0.451 0.638
## Residuals 116 21.248 0.18317print("L Delay")## [1] "L Delay"L_DELAY_LE_HPC_Ant.aov <- aov(L_DELAY_LE ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(L_DELAY_LE_HPC_Ant.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.025 0.01240 0.369 0.692
## Residuals 116 3.896 0.03359print("R Delay")## [1] "R Delay"R_DELAY_LE_HPC_Ant.aov <- aov(R_DELAY_LE ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(R_DELAY_LE_HPC_Ant.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.004 0.001886 0.061 0.941
## Residuals 116 3.588 0.030928print("L Probe")## [1] "L Probe"L_PROBE_LE_HPC_Ant.aov <- aov(L_PROBE_LE ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(L_PROBE_LE_HPC_Ant.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.20 0.1014 0.199 0.82
## Residuals 116 59.16 0.5100print("R Probe")## [1] "R Probe"R_PROBE_LE_HPC_Ant.aov <- aov(R_PROBE_LE ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(R_PROBE_LE_HPC_Ant.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.51 0.2553 0.521 0.595
## Residuals 116 56.84 0.4900Medial
L3
DFR_HPC_Med_plots[["L_CUE_L3"]] + DFR_HPC_Med_plots[["L_DELAY_L3"]] + DFR_HPC_Med_plots[["L_PROBE_L3"]]+
plot_annotation(title="HPC_Med during DFR task")
DFR_HPC_Med_plots[["R_CUE_L3"]] + DFR_HPC_Med_plots[["R_DELAY_L3"]] + DFR_HPC_Med_plots[["R_PROBE_L3"]]
print("L Cue")## [1] "L Cue"L_CUE_L3_HPC_Med.aov <- aov(L_CUE_L3 ~ level, data=split_DFR_HPC_Med[["all"]])
summary(L_CUE_L3_HPC_Med.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.099 0.04973 0.316 0.73
## Residuals 116 18.266 0.15746print("R Cue")## [1] "R Cue"R_CUE_L3_HPC_Med.aov <- aov(R_CUE_L3 ~ level, data=split_DFR_HPC_Med[["all"]])
summary(R_CUE_L3_HPC_Med.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.289 0.1444 0.908 0.406
## Residuals 116 18.450 0.1590print("L Delay")## [1] "L Delay"L_DELAY_L3_HPC_Med.aov <- aov(L_DELAY_L3 ~ level, data=split_DFR_HPC_Med[["all"]])
summary(L_DELAY_L3_HPC_Med.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0171 0.008561 0.381 0.684
## Residuals 116 2.6062 0.022467print("R Delay")## [1] "R Delay"R_DELAY_L3_HPC_Med.aov <- aov(R_DELAY_L3 ~ level, data=split_DFR_HPC_Med[["all"]])
summary(R_DELAY_L3_HPC_Med.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0707 0.03535 1.81 0.168
## Residuals 116 2.2654 0.01953print("L Probe")## [1] "L Probe"L_PROBE_L3_HPC_Med.aov <- aov(L_PROBE_L3 ~ level, data=split_DFR_HPC_Med[["all"]])
summary(L_PROBE_L3_HPC_Med.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.89 0.4462 1.025 0.362
## Residuals 116 50.48 0.4351print("R Probe")## [1] "R Probe"R_PROBE_L3_HPC_Med.aov <- aov(R_PROBE_L3 ~ level, data=split_DFR_HPC_Med[["all"]])
summary(R_PROBE_L3_HPC_Med.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.11 0.5540 1.232 0.295
## Residuals 116 52.16 0.4497LE
DFR_HPC_Med_plots[["L_CUE_LE"]] + DFR_HPC_Med_plots[["L_DELAY_LE"]] + DFR_HPC_Med_plots[["L_PROBE_LE"]]+
plot_annotation(title="HPC_Med during DFR task")
DFR_HPC_Med_plots[["R_CUE_LE"]] + DFR_HPC_Med_plots[["R_DELAY_LE"]] + DFR_HPC_Med_plots[["R_PROBE_LE"]]
print("L Cue")## [1] "L Cue"L_CUE_LE_HPC_Med.aov <- aov(L_CUE_LE ~ level, data=split_DFR_HPC_Med[["all"]])
summary(L_CUE_LE_HPC_Med.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.01 0.00489 0.049 0.952
## Residuals 116 11.51 0.09919print("R Cue")## [1] "R Cue"R_CUE_LE_HPC_Med.aov <- aov(R_CUE_LE ~ level, data=split_DFR_HPC_Med[["all"]])
summary(R_CUE_LE_HPC_Med.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.053 0.02625 0.236 0.79
## Residuals 116 12.919 0.11137print("L Delay")## [1] "L Delay"L_DELAY_LE_HPC_Med.aov <- aov(L_DELAY_LE ~ level, data=split_DFR_HPC_Med[["all"]])
summary(L_DELAY_LE_HPC_Med.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0314 0.01569 0.589 0.557
## Residuals 116 3.0904 0.02664print("R Delay")## [1] "R Delay"R_DELAY_LE_HPC_Med.aov <- aov(R_DELAY_LE ~ level, data=split_DFR_HPC_Med[["all"]])
summary(R_DELAY_LE_HPC_Med.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0022 0.001118 0.049 0.952
## Residuals 116 2.6589 0.022921print("L Probe")## [1] "L Probe"L_PROBE_LE_HPC_Med.aov <- aov(L_PROBE_LE ~ level, data=split_DFR_HPC_Med[["all"]])
summary(L_PROBE_LE_HPC_Med.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.67 0.3349 0.816 0.445
## Residuals 116 47.61 0.4104print("R Probe")## [1] "R Probe"R_PROBE_LE_HPC_Med.aov <- aov(R_PROBE_LE ~ level, data=split_DFR_HPC_Med[["all"]])
summary(R_PROBE_LE_HPC_Med.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.13 0.5658 1.713 0.185
## Residuals 116 38.31 0.3302Posterior
L3
DFR_HPC_Post_plots[["L_CUE_L3"]] + DFR_HPC_Post_plots[["L_DELAY_L3"]] + DFR_HPC_Post_plots[["L_PROBE_L3"]]+
plot_annotation(title="HPC_Post during DFR task")
DFR_HPC_Post_plots[["R_CUE_L3"]] + DFR_HPC_Post_plots[["R_DELAY_L3"]] + DFR_HPC_Post_plots[["R_PROBE_L3"]]
print("L Cue")## [1] "L Cue"L_CUE_L3_HPC_Post.aov <- aov(L_CUE_L3 ~ level, data=split_DFR_HPC_Post[["all"]])
summary(L_CUE_L3_HPC_Post.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.115 0.5577 4.26 0.0164 *
## Residuals 116 15.186 0.1309
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(L_CUE_L3_HPC_Post.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = L_CUE_L3 ~ level, data = split_DFR_HPC_Post[["all"]])
##
## $level
## diff lwr upr p adj
## med-high 0.2136174 0.02153523 0.405699632 0.0253429
## low-high 0.0188655 -0.17444407 0.212175078 0.9708461
## low-med -0.1947519 -0.38806150 -0.001442354 0.0478814print("R Cue")## [1] "R Cue"R_CUE_L3_HPC_Post.aov <- aov(R_CUE_L3 ~ level, data=split_DFR_HPC_Post[["all"]])
summary(R_CUE_L3_HPC_Post.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.667 0.3334 2.727 0.0696 .
## Residuals 116 14.181 0.1222
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1print("L Delay")## [1] "L Delay"L_DELAY_L3_HPC_Post.aov <- aov(L_DELAY_L3 ~ level, data=split_DFR_HPC_Post[["all"]])
summary(L_DELAY_L3_HPC_Post.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0579 0.02895 2.053 0.133
## Residuals 116 1.6353 0.01410print("R Delay")## [1] "R Delay"R_DELAY_L3_HPC_Post.aov <- aov(R_DELAY_L3 ~ level, data=split_DFR_HPC_Post[["all"]])
summary(R_DELAY_L3_HPC_Post.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0276 0.01379 1.182 0.31
## Residuals 116 1.3534 0.01167print("L Probe")## [1] "L Probe"L_PROBE_L3_HPC_Post.aov <- aov(L_PROBE_L3 ~ level, data=split_DFR_HPC_Post[["all"]])
summary(L_PROBE_L3_HPC_Post.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 2.87 1.4366 5.043 0.00794 **
## Residuals 116 33.04 0.2849
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(L_PROBE_L3_HPC_Post.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = L_PROBE_L3 ~ level, data = split_DFR_HPC_Post[["all"]])
##
## $level
## diff lwr upr p adj
## med-high 0.2358601 -0.04748753 0.51920767 0.1226631
## low-high -0.1412081 -0.42636627 0.14395003 0.4700479
## low-med -0.3770682 -0.66222634 -0.09191004 0.0060407print("R Probe")## [1] "R Probe"R_PROBE_L3_HPC_Post.aov <- aov(R_PROBE_L3 ~ level, data=split_DFR_HPC_Post[["all"]])
summary(R_PROBE_L3_HPC_Post.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 2.06 1.0315 3.368 0.0379 *
## Residuals 116 35.53 0.3063
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(R_PROBE_L3_HPC_Post.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = R_PROBE_L3 ~ level, data = split_DFR_HPC_Post[["all"]])
##
## $level
## diff lwr upr p adj
## med-high 0.1935997 -0.1002181 0.48741756 0.2652144
## low-high -0.1270240 -0.4227192 0.16867132 0.5658518
## low-med -0.3206237 -0.6163190 -0.02492841 0.0301680LE
DFR_HPC_Post_plots[["L_CUE_LE"]] + DFR_HPC_Post_plots[["L_DELAY_LE"]] + DFR_HPC_Post_plots[["L_PROBE_LE"]]+
plot_annotation(title="HPC_Post during DFR task")
DFR_HPC_Post_plots[["R_CUE_LE"]] + DFR_HPC_Post_plots[["R_DELAY_LE"]] + DFR_HPC_Post_plots[["R_PROBE_LE"]]
print("L Cue")## [1] "L Cue"L_CUE_LE_HPC_Post.aov <- aov(L_CUE_LE ~ level, data=split_DFR_HPC_Post[["all"]])
summary(L_CUE_LE_HPC_Post.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.223 0.11161 1.386 0.254
## Residuals 116 9.341 0.08053print("R Cue")## [1] "R Cue"R_CUE_LE_HPC_Post.aov <- aov(R_CUE_LE ~ level, data=split_DFR_HPC_Post[["all"]])
summary(R_CUE_LE_HPC_Post.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.290 0.1449 2.147 0.121
## Residuals 116 7.829 0.0675print("L Delay")## [1] "L Delay"L_DELAY_LE_HPC_Post.aov <- aov(L_DELAY_LE ~ level, data=split_DFR_HPC_Post[["all"]])
summary(L_DELAY_LE_HPC_Post.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0021 0.001067 0.051 0.95
## Residuals 116 2.4087 0.020764print("R Delay")## [1] "R Delay"R_DELAY_LE_HPC_Post.aov <- aov(R_DELAY_LE ~ level, data=split_DFR_HPC_Post[["all"]])
summary(R_DELAY_LE_HPC_Post.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.002 0.000985 0.068 0.935
## Residuals 116 1.687 0.014546print("L Probe")## [1] "L Probe"L_PROBE_LE_HPC_Post.aov <- aov(L_PROBE_LE ~ level, data=split_DFR_HPC_Post[["all"]])
summary(L_PROBE_LE_HPC_Post.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.13 0.5645 2.037 0.135
## Residuals 116 32.15 0.2771print("R Probe")## [1] "R Probe"R_PROBE_LE_HPC_Post.aov <- aov(R_PROBE_LE ~ level, data=split_DFR_HPC_Post[["all"]])
summary(R_PROBE_LE_HPC_Post.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.437 0.7184 2.796 0.0652 .
## Residuals 116 29.804 0.2569
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Cortical Thickness
Only see differences in the L probe regions, with med > high.
cortical_thickness_plots[["Cue_RH"]] + cortical_thickness_plots[["Delay_RH"]] + cortical_thickness_plots[["Probe_RH"]] +
plot_annotation(title="Cortical Thickness from DFR Full Mask") 
cortical_thickness_plots[["Cue_LH"]] + cortical_thickness_plots[["Delay_LH"]] + cortical_thickness_plots[["Probe_LH"]]
print("L Cue")## [1] "L Cue"L_CUE_DFR_thick.aov <- aov(Cue_LH ~ level, data=split_cortical_thickness_DFR[["all"]])
summary(L_CUE_DFR_thick.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0037 0.001853 0.199 0.82
## Residuals 116 1.0818 0.009326print("R Cue")## [1] "R Cue"R_CUE_DFR_thick.aov <- aov(Cue_RH ~ level, data=split_cortical_thickness_DFR[["all"]])
summary(R_CUE_LE_HPC_Post.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.290 0.1449 2.147 0.121
## Residuals 116 7.829 0.0675print("L Delay")## [1] "L Delay"L_DELAY_DFR_thick.aov <- aov(Delay_LH ~ level, data=split_cortical_thickness_DFR[["all"]])
summary(L_DELAY_DFR_thick.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0366 0.01829 0.804 0.45
## Residuals 116 2.6380 0.02274print("R Delay")## [1] "R Delay"R_DELAY_DFR_thick.aov <- aov(Delay_RH ~ level, data=split_cortical_thickness_DFR[["all"]])
summary(R_DELAY_DFR_thick.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0012 0.000591 0.034 0.966
## Residuals 116 2.0093 0.017322print("L Probe")## [1] "L Probe"L_PROBE_DFR_thick.aov <- aov(Probe_LH ~ level, data=split_cortical_thickness_DFR[["all"]])
summary(L_PROBE_DFR_thick.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0294 0.01472 0.862 0.425
## Residuals 116 1.9809 0.01708print("R Probe")## [1] "R Probe"R_PROBE_DFR_thick.aov <- aov(Probe_RH ~ level, data=split_cortical_thickness_DFR[["all"]])
summary(R_PROBE_DFR_thick.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0124 0.006181 0.333 0.717
## Residuals 116 2.1502 0.018536Resting State Functional Connectivity
Within Network
No differences.
RS_plots[["FPCN_FPCN"]] + RS_plots[["DMN_DMN"]] + RS_plots[["DAN_DAN"]]+
plot_annotation(title="Resting State Functional Connectivity - Within Networks")
RS_plots[["VAN_VAN"]] + RS_plots[["CO_CO"]] + RS_plots[["visual_visual"]]
print("FPCN")## [1] "FPCN"FPCN.aov <- aov(FPCN_FPCN ~ level, data=split_RS[["all"]])
summary(FPCN.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0010 0.000493 0.062 0.939
## Residuals 116 0.9151 0.007888print("DMN")## [1] "DMN"DMN.aov <- aov(DMN_DMN ~ level, data=split_RS[["all"]])
summary(DMN.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0112 0.005622 0.562 0.572
## Residuals 116 1.1607 0.010006print("DAN")## [1] "DAN"DAN.aov <- aov(DAN_DAN ~ level, data=split_RS[["all"]])
summary(DAN.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0031 0.001532 0.249 0.78
## Residuals 116 0.7133 0.006149print("VAN")## [1] "VAN"VAN.aov <- aov(VAN_VAN ~ level, data=split_RS[["all"]])
summary(VAN.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.043 0.021483 2.475 0.0886 .
## Residuals 116 1.007 0.008679
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1print("CO")## [1] "CO"CO.aov <- aov(CO_CO ~ level, data=split_RS[["all"]])
summary(CO.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0006 0.000289 0.04 0.961
## Residuals 116 0.8374 0.007219print("CO")## [1] "CO"visual.aov <- aov(visual_visual ~ level, data=split_RS[["all"]])
summary(visual.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.019 0.009497 0.528 0.591
## Residuals 116 2.086 0.017981Across Network
No across RS network differences.
RS_plots[["FPCN_DMN"]] + RS_plots[["FPCN_DAN"]] + RS_plots[["FCPN_VAN"]]+
plot_annotation(title="Resting State Functional Connectivity - Across Networks")
RS_plots[["FPCN_CO"]] + RS_plots[["FPCN_visual"]]
print("FPCN DMN")## [1] "FPCN DMN"FPCN_DMN.aov <- aov(FPCN_DMN ~ level, data=split_RS[["all"]])
summary(FPCN_DMN.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0155 0.007775 1.002 0.37
## Residuals 116 0.8997 0.007756print("FPCN DAN")## [1] "FPCN DAN"FPCN_DAN.aov <- aov(FPCN_DAN ~ level, data=split_RS[["all"]])
summary(FPCN_DAN.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0034 0.001689 0.374 0.689
## Residuals 116 0.5237 0.004515print("FPCN VAN")## [1] "FPCN VAN"FPCN_VAN.aov <- aov(FPCN_VAN ~ level, data=split_RS[["all"]])
summary(FPCN_VAN.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0189 0.009461 1.505 0.226
## Residuals 116 0.7290 0.006284print("FPCN CO")## [1] "FPCN CO"FPCN_CO.aov <- aov(FPCN_CO ~ level, data=split_RS[["all"]])
summary(FPCN_CO.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0507 0.025374 2.62 0.0771 .
## Residuals 116 1.1234 0.009685
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1print("FPCN visual")## [1] "FPCN visual"FPCN_visual.aov <- aov(FPCN_visual ~ level, data=split_RS[["all"]])
summary(FPCN_visual.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0374 0.018712 3.013 0.053 .
## Residuals 116 0.7205 0.006211
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Beta Series Connectivity
Cue Period
High Load
No differences.
beta_conn_cue_plots[["FPCN_FPCN_L3"]] + beta_conn_cue_plots[["FPCN_HPC_L3"]] +
plot_annotation(title = "Beta Series Connectivity at High Load")
beta_conn_cue_plots[["FPCN_FFA_L3"]] + beta_conn_cue_plots[["HPC_FFA_L3"]]
FPCN_FPCN_BC_cue_L3.aov <- aov(FPCN_FPCN_L3 ~ level, data = split_beta_conn_cue[["all"]])
summary(FPCN_FPCN_BC_cue_L3.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.193 0.09666 0.779 0.461
## Residuals 116 14.397 0.12411FPCN_HPC_BC_cue_L3.aov <- aov(FPCN_HPC_L3 ~ level, data = split_beta_conn_cue[["all"]])
summary(FPCN_HPC_BC_cue_L3.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.043 0.02156 0.206 0.814
## Residuals 116 12.146 0.10471FPCN_FFA_BC_cue_L3.aov <- aov(FPCN_FFA_L3 ~ level, data = split_beta_conn_cue[["all"]])
summary(FPCN_FFA_BC_cue_L3.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.030 0.01476 0.187 0.83
## Residuals 116 9.161 0.07897HPC_FFA_BC_cue_L3.aov <- aov(HPC_FFA_L3 ~ level, data = split_beta_conn_cue[["all"]])
summary(HPC_FFA_BC_cue_L3.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.056 0.02784 0.28 0.757
## Residuals 116 11.551 0.09958Load Effect
No differences.
beta_conn_cue_plots[["FPCN_FPCN_LE"]] + beta_conn_cue_plots[["FPCN_HPC_LE"]] +
plot_annotation(title = "Beta Series Connectivity Load Effect")
beta_conn_cue_plots[["FPCN_FFA_LE"]] + beta_conn_cue_plots[["HPC_FFA_LE"]]
FPCN_FPCN_BC_cue_LE.aov <- aov(FPCN_FPCN_LE ~ level, data = split_beta_conn_cue[["all"]])
summary(FPCN_FPCN_BC_cue_LE.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.568 0.2839 1.678 0.191
## Residuals 116 19.628 0.1692FPCN_HPC_BC_cue_LE.aov <- aov(FPCN_HPC_LE ~ level, data = split_beta_conn_cue[["all"]])
summary(FPCN_HPC_BC_cue_LE.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.085 0.04247 0.343 0.71
## Residuals 116 14.345 0.12367FPCN_FFA_BC_cue_LE.aov <- aov(FPCN_FFA_LE ~ level, data = split_beta_conn_cue[["all"]])
summary(FPCN_FFA_BC_cue_LE.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.08 0.03981 0.234 0.792
## Residuals 116 19.71 0.16996HPC_FFA_BC_cue_LE.aov <- aov(HPC_FFA_LE ~ level, data = split_beta_conn_cue[["all"]])
summary(HPC_FFA_BC_cue_LE.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.225 0.1123 0.681 0.508
## Residuals 116 19.133 0.1649Delay Period
No differences for the beta series connectivity during delay period.
High Load
beta_conn_delay_plots[["FPCN_FPCN_L3"]] + beta_conn_delay_plots[["FPCN_HPC_L3"]] +
plot_annotation(title = "Beta Series Connectivity at High Load")
beta_conn_delay_plots[["FPCN_FFA_L3"]] + beta_conn_delay_plots[["HPC_FFA_L3"]]
FPCN_FPCN_BC_delay_L3.aov <- aov(FPCN_FPCN_L3 ~ level, data = split_beta_conn_delay[["all"]])
summary(FPCN_FPCN_BC_delay_L3.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.25 0.12487 1.402 0.25
## Residuals 116 10.33 0.08907FPCN_HPC_BC_delay_L3.aov <- aov(FPCN_HPC_L3 ~ level, data = split_beta_conn_delay[["all"]])
summary(FPCN_HPC_BC_delay_L3.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.219 0.1093 1.757 0.177
## Residuals 116 7.215 0.0622FPCN_FFA_BC_delay_L3.aov <- aov(FPCN_FFA_L3 ~ level, data = split_beta_conn_delay[["all"]])
summary(FPCN_FFA_BC_delay_L3.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.043 0.02170 0.364 0.696
## Residuals 116 6.920 0.05965HPC_FFA_BC_delay_L3.aov <- aov(HPC_FFA_L3 ~ level, data = split_beta_conn_delay[["all"]])
summary(HPC_FFA_BC_delay_L3.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.170 0.08476 1.387 0.254
## Residuals 116 7.088 0.06111Load Effect
Differences between the HPC/FFA connectivity - low > med.
beta_conn_delay_plots[["FPCN_FPCN_LE"]] + beta_conn_delay_plots[["FPCN_HPC_LE"]] +
plot_annotation(title = "Beta Series Connectivity Load Effect")
beta_conn_delay_plots[["FPCN_FFA_LE"]] + beta_conn_delay_plots[["HPC_FFA_LE"]]
FPCN_FPCN_BC_delay_LE.aov <- aov(FPCN_FPCN_LE ~ level, data = split_beta_conn_delay[["all"]])
summary(FPCN_FPCN_BC_delay_LE.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.050 0.02517 0.536 0.587
## Residuals 116 5.452 0.04700FPCN_HPC_BC_delay_LE.aov <- aov(FPCN_HPC_LE ~ level, data = split_beta_conn_delay[["all"]])
summary(FPCN_HPC_BC_delay_LE.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.095 0.04745 0.745 0.477
## Residuals 116 7.389 0.06370FPCN_FFA_BC_delay_LE.aov <- aov(FPCN_FFA_LE ~ level, data = split_beta_conn_delay[["all"]])
summary(FPCN_FFA_BC_delay_LE.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.114 0.05696 0.769 0.466
## Residuals 116 8.594 0.07408HPC_FFA_BC_delay_LE.aov <- aov(HPC_FFA_LE ~ level, data = split_beta_conn_delay[["all"]])
summary(HPC_FFA_BC_delay_LE.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.821 0.4105 4.664 0.0113 *
## Residuals 116 10.210 0.0880
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(HPC_FFA_BC_delay_LE.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = HPC_FFA_LE ~ level, data = split_beta_conn_delay[["all"]])
##
## $level
## diff lwr upr p adj
## med-high 0.07253329 -0.08496399 0.23003057 0.5200801
## low-high -0.12896551 -0.28746918 0.02953816 0.1343892
## low-med -0.20149880 -0.36000247 -0.04299513 0.0087169BCT Measures
No differences in any of the BCT measures.
Overall Measures
BCT_plots[["Participation_Coef_Mean"]] + BCT_plots[["Global_Eff"]] + BCT_plots[["Modularity_Louvain_N"]]+
plot_annotation(title="Overall BCT Measures")
print("Mean Participation Coefficient")## [1] "Mean Participation Coefficient"partic_coef_mean.aov <- aov(Participation_Coef_Mean ~ level, data = split_BCT[["all"]])
summary(partic_coef_mean.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.125 0.06255 0.859 0.426
## Residuals 116 8.446 0.07281print("Global Efficiency")## [1] "Global Efficiency"global_eff.aov <- aov(Global_Eff ~ level, data = split_BCT[["all"]])
summary(global_eff.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0138 0.006884 1.857 0.161
## Residuals 116 0.4301 0.003708print("Modularity")## [1] "Modularity"modularity.aov <- aov(Modularity_Louvain_N ~ level, data = split_BCT[["all"]])
summary(modularity.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.3 0.128 0.017 0.983
## Residuals 116 869.9 7.499Individual Networks
indiv_partic_coeff_plots[["FrontoParietal"]] + indiv_partic_coeff_plots[["Default"]] + indiv_partic_coeff_plots[["DorsalAttn"]]+
plot_annotation(title="Individual Network Participation Coefficient")
indiv_partic_coeff_plots[["CinguloOperc"]] + indiv_partic_coeff_plots[["VentralAttn"]] + indiv_partic_coeff_plots[["Visual"]]
print("FPCN")## [1] "FPCN"FPCN_indiv_coeff.aov <- aov(FrontoParietal ~ level, data = split_indiv_partic_coeff[["all"]])
summary(FPCN_indiv_coeff.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.514 0.2568 1.391 0.253
## Residuals 116 21.411 0.1846print("DMN")## [1] "DMN"DMN_indiv_coeff.aov <- aov(Default ~ level, data = split_indiv_partic_coeff[["all"]])
summary(DMN_indiv_coeff.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.119 0.5597 2.784 0.0659 .
## Residuals 116 23.319 0.2010
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1print("DAN")## [1] "DAN"DAN_indiv_coeff.aov <- aov(DorsalAttn ~ level, data = split_indiv_partic_coeff[["all"]])
summary(DAN_indiv_coeff.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.043 0.02172 0.237 0.789
## Residuals 116 10.623 0.09158print("CO")## [1] "CO"CO_indiv_coeff.aov <- aov(CinguloOperc ~ level, data = split_indiv_partic_coeff[["all"]])
summary(CO_indiv_coeff.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.318 0.1588 1.354 0.262
## Residuals 116 13.611 0.1173print("VAN")## [1] "VAN"VAN_indiv_coeff.aov <- aov(VentralAttn ~ level, data = split_indiv_partic_coeff[["all"]])
summary(VAN_indiv_coeff.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.50 0.2499 2.108 0.126
## Residuals 116 13.75 0.1185print("visual")## [1] "visual"visual_indiv_coeff.aov <- aov(Visual ~ level, data = split_indiv_partic_coeff[["all"]])
summary(visual_indiv_coeff.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.62 0.3102 1.918 0.151
## Residuals 116 18.75 0.1617